JSM 2021 Online Program

Motivated by the idea of reduced rank estimation originally developed for non-mixture cases, we here study reduced rank mixtures of multivariate response regression models to provide more parsimonious and interpretable models. The proposed estimator simultaneously take take into account the joint structure of the multivariate response and population heterogeneity. We show the complete derivation of iterative algorithms that perform parameter estimation in mixtures of multivariate response regression models with and without the reduced rank framework. Via the proposed paradigm, we have some desired features such as monotonicity of the penalized likelihood sequence. The consistency of the proposed estimators is established. The performances of the proposed reduced rank methods are evaluated through simulation studies and real data analysis.